class_docstr: Strategy for distributed training using torch.distributed.
code: def get_model_state_dict_shard(self, model: nn.Module, **config):
      # TODO: implement sharding on naive strategy
      model = self.unwrap_model(model)
      if (
          "requires_grad_only" in config
          and config["requires_grad_only"] == True
      ):
          state_dict = get_grad_required_state_dict(model)
      else:
          state_dict = model.state_dict()
  
      if "shard_size" in config:
          shard_size = config["shard_size"]
          accumulate_size = 0
          state_dict_shard = OrderedDict()
          for name, param in state_dict.items():
              state_dict_shard[name] = param
              accumulate_size += param.numel() * param.element_size()
              if accumulate_size >= shard_size:
                  accumulate_size = 0
                  yield state_dict_shard
                  state_dict_shard = OrderedDict()
          if accumulate_size > 0:
              yield state_dict_shard
      else:
          yield state_dict
  
imports: import os
  import random
  from collections import OrderedDict
  from typing import Callable, Optional
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  from coati.experience_buffer import ExperienceBuffer
  from coati.models import Actor, Critic, RewardModel
  from torch.utils.data import DataLoader
  from transformers.modeling_utils import PreTrainedModel
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  from colossalai.booster.plugin import TorchDDPPlugin
  from colossalai.booster.plugin.torch_ddp_plugin import TorchDDPModel
  from .base import Strategy
  from .sampler import DistributedSampler
label_desc: Use image node_id 10 for calling the DDPStrategy obj's underlying member method code with example usage: obj.get_model_state_dict_shard(model) without return types